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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/manabil/Tensorflow-Advanced-Techniques-Specialization/blob/main/Custom%20and%20Distributed%20Training%20with%20Tensorflow/Week%201/C2W1_Assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BQRxz8e7wvxv"
+      },
+      "source": [
+        "# Basic Tensor operations and GradientTape.\n",
+        "\n",
+        "In this graded assignment, you will perform different tensor operations as well as use [GradientTape](https://www.tensorflow.org/api_docs/python/tf/GradientTape). These are important building blocks for the next parts of this course so it's important to master the basics. Let's begin!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "jqev488WJ9-R"
+      },
+      "outputs": [],
+      "source": [
+        "import tensorflow as tf\n",
+        "import numpy as np"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oL4Vni_Lwvx_"
+      },
+      "source": [
+        "## Exercise 1 - [tf.constant]((https://www.tensorflow.org/api_docs/python/tf/constant))\n",
+        "\n",
+        "Creates a constant tensor from a tensor-like object."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "MYdVyiSoLPgO"
+      },
+      "outputs": [],
+      "source": [
+        "# Convert NumPy array to Tensor using `tf.constant`\n",
+        "def tf_constant(array: np.ndarray) -> tf.Tensor:\n",
+        "    \"\"\"\n",
+        "    Args:\n",
+        "        array (numpy.ndarray): tensor-like array.\n",
+        "\n",
+        "    Returns:\n",
+        "        tensorflow.python.framework.ops.EagerTensor: tensor.\n",
+        "    \"\"\"\n",
+        "    ### START CODE HERE ###\n",
+        "    tf_constant_array: tf.Tensor = tf.constant(value=array)\n",
+        "    ### END CODE HERE ###\n",
+        "    return tf_constant_array"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sSlr-uQQwvyI",
+        "outputId": "74fd006a-a571-4d62-ff02-23bf39e56df0"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<tf.Tensor: shape=(9,), dtype=int64, numpy=array([1, 2, 3, 4, 5, 6, 7, 8, 9])>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ],
+      "source": [
+        "tmp_array: np.ndarray = np.arange(1,10)\n",
+        "x: tf.Tensor = tf_constant(tmp_array)\n",
+        "x\n",
+        "\n",
+        "# Expected output:\n",
+        "# <tf.Tensor: shape=(9,),\n",
+        "# dtype=int64,\n",
+        "# numpy=array([1, 2, 3, 4, 5, 6, 7, 8, 9])>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UjnP9hdVwvyM"
+      },
+      "source": [
+        "Note that for future docstrings, the type `EagerTensor` will be used as a shortened version of `tensorflow.python.framework.ops.EagerTensor`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FnNvdTH_wvyP"
+      },
+      "source": [
+        "## Exercise 2 - [tf.square](https://www.tensorflow.org/api_docs/python/tf/math/square)\n",
+        "\n",
+        "Computes the square of a tensor element-wise."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "W6BTwNJCLjV8"
+      },
+      "outputs": [],
+      "source": [
+        "# Square the input tensor\n",
+        "def tf_square(array: tf.Tensor) -> tf.Tensor:\n",
+        "    \"\"\"\n",
+        "    Args:\n",
+        "        array (numpy.ndarray): tensor-like array.\n",
+        "\n",
+        "    Returns:\n",
+        "        EagerTensor: tensor.\n",
+        "    \"\"\"\n",
+        "    # make sure it's a tensor\n",
+        "    array = tf.constant(array)\n",
+        "\n",
+        "    ### START CODE HERE ###\n",
+        "    tf_squared_array: tf.Tensor = tf.square(x=array)\n",
+        "    ### END CODE HERE ###\n",
+        "    return tf_squared_array"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0wgBxzptwvyZ",
+        "outputId": "ab5e408d-3a7b-479f-bcc4-e28c8ab068a4"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<tf.Tensor: shape=(9,), dtype=int64, numpy=array([ 1,  4,  9, 16, 25, 36, 49, 64, 81])>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ],
+      "source": [
+        "tmp_array: tf.Tensor = tf.constant(np.arange(1, 10))\n",
+        "x: tf.Tensor = tf_square(tmp_array)\n",
+        "x\n",
+        "\n",
+        "# Expected output:\n",
+        "# <tf.Tensor: shape=(9,),\n",
+        "# dtype=int64,\n",
+        "# numpy=array([ 1,  4,  9, 16, 25, 36, 49, 64, 81])>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YCKmMPpIwvyg"
+      },
+      "source": [
+        "## Exercise 3 - [tf.reshape](https://www.tensorflow.org/api_docs/python/tf/reshape)\n",
+        "\n",
+        "Reshapes a tensor."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "7nzBSX8-L0Xt"
+      },
+      "outputs": [],
+      "source": [
+        "# Reshape tensor into the given shape parameter\n",
+        "def tf_reshape(array: np.ndarray, shape: tuple[int, int]) -> tf.Tensor:\n",
+        "    \"\"\"\n",
+        "    Args:\n",
+        "        array (EagerTensor): tensor to reshape.\n",
+        "        shape (tuple): desired shape.\n",
+        "\n",
+        "    Returns:\n",
+        "        EagerTensor: reshaped tensor.\n",
+        "    \"\"\"\n",
+        "    # make sure it's a tensor\n",
+        "    array = tf.constant(array)\n",
+        "    ### START CODE HERE ###\n",
+        "    tf_reshaped_array: tf.Tensor = tf.reshape(tensor=array, shape=shape)\n",
+        "    ### END CODE HERE ###\n",
+        "    return tf_reshaped_array"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IcuDGLdDwvyq",
+        "outputId": "837d12c1-bc70-48f2-e879-547738b3696d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<tf.Tensor: shape=(3, 3), dtype=int64, numpy=\n",
+              "array([[1, 2, 3],\n",
+              "       [4, 5, 6],\n",
+              "       [7, 8, 9]])>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ],
+      "source": [
+        "# Check your function\n",
+        "tmp_array: np.ndarray = np.array([1,2,3,4,5,6,7,8,9])\n",
+        "# Check that your function reshapes a vector into a matrix\n",
+        "x: tf.Tensor = tf_reshape(tmp_array, (3, 3))\n",
+        "x\n",
+        "\n",
+        "# Expected output:\n",
+        "# <tf.Tensor: shape=(3, 3), dtype=int64, numpy=\n",
+        "# [[1, 2, 3],\n",
+        "# [4, 5, 6],\n",
+        "# [7, 8, 9]]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-lR5mwxUwvyt"
+      },
+      "source": [
+        "## Exercise 4 - [tf.cast](https://www.tensorflow.org/api_docs/python/tf/cast)\n",
+        "\n",
+        "Casts a tensor to a new type."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "VoT-jiAIL8x5"
+      },
+      "outputs": [],
+      "source": [
+        "# Cast tensor into the given dtype parameter\n",
+        "def tf_cast(array: list, dtype: tf.DType) -> tf.Tensor:\n",
+        "    \"\"\"\n",
+        "    Args:\n",
+        "        array (EagerTensor): tensor to be casted.\n",
+        "        dtype (tensorflow.python.framework.dtypes.DType): desired new type.\n",
+        "        (Should be a TF dtype!)\n",
+        "\n",
+        "    Returns:\n",
+        "        EagerTensor: casted tensor.\n",
+        "    \"\"\"\n",
+        "    # make sure it's a tensor\n",
+        "    array = tf.constant(array)\n",
+        "\n",
+        "    ### START CODE HERE ###\n",
+        "    tf_cast_array = tf.cast(x=array, dtype=dtype)\n",
+        "    ### END CODE HERE ###\n",
+        "    return tf_cast_array"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ffQj9tX9wvzN",
+        "outputId": "982f03c1-5e6c-4cf3-d7f5-502c0e44d589"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<tf.Tensor: shape=(4,), dtype=float32, numpy=array([1., 2., 3., 4.], dtype=float32)>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ],
+      "source": [
+        "# Check your function\n",
+        "tmp_array: list = [1,2,3,4]\n",
+        "x: tf.Tensor = tf_cast(tmp_array, tf.float32)\n",
+        "x\n",
+        "\n",
+        "# Expected output:\n",
+        "# <tf.Tensor: shape=(4,), dtype=float32, numpy=array([1., 2., 3., 4.])>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GyTwYoZYwvzP"
+      },
+      "source": [
+        "## Exercise 5 - [tf.multiply](https://www.tensorflow.org/api_docs/python/tf/multiply)\n",
+        "\n",
+        "Returns an element-wise x * y."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "ivepGtD5MKP5"
+      },
+      "outputs": [],
+      "source": [
+        "# Multiply tensor1 and tensor2\n",
+        "def tf_multiply(tensor1: tf.Tensor, tensor2: tf.Tensor) -> tf.Tensor:\n",
+        "    \"\"\"\n",
+        "    Args:\n",
+        "        tensor1 (EagerTensor): a tensor.\n",
+        "        tensor2 (EagerTensor): another tensor.\n",
+        "\n",
+        "    Returns:\n",
+        "        EagerTensor: resulting tensor.\n",
+        "    \"\"\"\n",
+        "    # make sure these are tensors\n",
+        "    tensor1 = tf.constant(tensor1)\n",
+        "    tensor2 = tf.constant(tensor2)\n",
+        "\n",
+        "    ### START CODE HERE ###\n",
+        "    product: tf.Tensor = tf.multiply(x=tensor1, y=tensor2)\n",
+        "    ### END CODE HERE ###\n",
+        "    return product\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8EHKJYlvwvzW",
+        "outputId": "08836282-996a-4bb8-b9da-6650817d1ce7"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<tf.Tensor: shape=(2, 2), dtype=int64, numpy=\n",
+              "array([[2, 4],\n",
+              "       [6, 8]])>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ],
+      "source": [
+        "# Check your function\n",
+        "tmp_1: tf.Tensor = tf.constant(np.array([[1,2],[3,4]]))\n",
+        "tmp_2: tf.Tensor = tf.constant(np.array(2))\n",
+        "result: tf.Tensor = tf_multiply(tmp_1, tmp_2)\n",
+        "result\n",
+        "\n",
+        "# Expected output:\n",
+        "# <tf.Tensor: shape=(2, 2), dtype=int64, numpy=\n",
+        "# array([[2, 4],\n",
+        "#        [6, 8]])>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i4HjosUjwvzY"
+      },
+      "source": [
+        "## Exercise 6 - [tf.add](https://www.tensorflow.org/api_docs/python/tf/add)\n",
+        "\n",
+        "Returns x + y element-wise."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "BVlntdYnMboh"
+      },
+      "outputs": [],
+      "source": [
+        "# Add tensor1 and tensor2\n",
+        "def tf_add(tensor1: tf.Tensor, tensor2: tf.Tensor) -> tf.Tensor:\n",
+        "    \"\"\"\n",
+        "    Args:\n",
+        "        tensor1 (EagerTensor): a tensor.\n",
+        "        tensor2 (EagerTensor): another tensor.\n",
+        "\n",
+        "    Returns:\n",
+        "        EagerTensor: resulting tensor.\n",
+        "    \"\"\"\n",
+        "    # make sure these are tensors\n",
+        "    tensor1 = tf.constant(tensor1)\n",
+        "    tensor2 = tf.constant(tensor2)\n",
+        "\n",
+        "    ### START CODE HERE ###\n",
+        "    total: tf.Tensor = tf.add(x=tensor1, y=tensor2)\n",
+        "    ### END CODE HERE ###\n",
+        "    return total"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7ihhqavMwvzd",
+        "outputId": "8c44457b-e287-409b-d538-82020f62b9ec"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<tf.Tensor: shape=(3,), dtype=int64, numpy=array([5, 7, 9])>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ],
+      "source": [
+        "# Check your function\n",
+        "tmp_1: tf.Tensor = tf.constant(np.array([1, 2, 3]))\n",
+        "tmp_2: tf.Tensor = tf.constant(np.array([4, 5, 6]))\n",
+        "tf_add(tmp_1, tmp_2)\n",
+        "\n",
+        "# Expected output:\n",
+        "# <tf.Tensor: shape=(3,), dtype=int64, numpy=array([5, 7, 9])>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9EN0W15EWNjD"
+      },
+      "source": [
+        "## Exercise 7 - Gradient Tape\n",
+        "\n",
+        "Implement the function `tf_gradient_tape` by replacing the instances of `None` in the code below. The instructions are given in the code comments.\n",
+        "\n",
+        "You can review the [docs](https://www.tensorflow.org/api_docs/python/tf/GradientTape) or revisit the lectures to complete this task."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "p3K94BWZM6nW"
+      },
+      "outputs": [],
+      "source": [
+        "def tf_gradient_tape(x: tf.Tensor) -> tf.Tensor:\n",
+        "    \"\"\"\n",
+        "    Args:\n",
+        "        x (EagerTensor): a tensor.\n",
+        "\n",
+        "    Returns:\n",
+        "        EagerTensor: Derivative of z with respect to the input tensor x.\n",
+        "    \"\"\"\n",
+        "    with tf.GradientTape() as t:\n",
+        "\n",
+        "    ### START CODE HERE ###\n",
+        "        # Record the actions performed on tensor x with `watch`\n",
+        "        t.watch(x)\n",
+        "\n",
+        "        # Define a polynomial of form 3x^3 - 2x^2 + x\n",
+        "        y: tf.Tensor = (3*(x ** 3)) - (2*(x ** 2)) + x\n",
+        "\n",
+        "        # Obtain the sum of the elements in variable y\n",
+        "        z: tf.Tensor = tf.reduce_sum(y)\n",
+        "\n",
+        "    # Get the derivative of z with respect to the original input tensor x\n",
+        "    dz_dx: tf.Tensor = t.gradient(target=z, sources=x)\n",
+        "    ### END CODE HERE\n",
+        "\n",
+        "    return dz_dx"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nat6WZ4fwvzk",
+        "outputId": "a8e78139-0d62-4d65-a670-b6116b16c53e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "29.0"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ],
+      "source": [
+        "# Check your function\n",
+        "tmp_x: tf.Tensor = tf.constant(2.0)\n",
+        "dz_dx: tf.Tensor = tf_gradient_tape(tmp_x)\n",
+        "result: np.ndarray = dz_dx.numpy()\n",
+        "result\n",
+        "\n",
+        "# Expected output:\n",
+        "# 29.0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sq_GgW2uwvzn"
+      },
+      "source": [
+        "**Congratulations on finishing this week's assignment!**\n",
+        "\n",
+        "**Keep it up!**"
+      ]
+    }
+  ],
+  "metadata": {
+    "coursera": {
+      "schema_names": [
+        "TF3C2W1-1",
+        "TF3C2W1-2",
+        "TF3C2W1-3",
+        "TF3C2W1-4",
+        "TF3C2W1-5",
+        "TF3C2W1-6",
+        "TF3C2W1-7"
+      ]
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.6"
+    },
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file